一种基于间隙度特征的SAR图像车辆目标鉴别算法

该文提出一种新的基于间隙度(lacunarity)特征的高分辨率SAR图像车辆目标鉴别算法，用以去除检测阶段的自然杂波虚警。文中着重分析了高频区车辆目标和自然地物后向散射强度不规则性的差异，其中与自然地物相比，车辆目标像素集合在灰度图像上表现为较强的不规则性和较大的间隙尺寸。基于这一特点，利用分形理论提取间隙度特征来定量估算待鉴别目标像素强度分布的不规则性和间隙大小，并以此实现鉴别处理。最后，采用X波段的两种实测图像数据验证了该文算法，结果显示该特征具有较好的鉴别性能。

An Algorithm of Vehicle Target Discrimination in SAR Imagery with Lacunarity Feature

A new algorithm which can use lacunarity feature to discriminate vehicle target from natural clutter in SAR imagery is developed in this paper. Firstly, the variation and irregularity of back-scattered intensity for vehicle target and natural terrain are analyzed, which are resulted from their different scattering centers with different spatial arrangement and other cases. The vehicle image presents more irregularity and largeness of gaps than natural terrain’s image. Based on fractal theory, the lacunarity feature is estimated to measure the difference and can be used to eliminate the natural clutter. And then, the real X band SAR image data are applied to validate the above algorithm, and the performance of this algorithm is good.